def __init__(self, params, num_gpu):
super(LMDBDataUnit, self).__init__(params, num_gpu)
if params.include[0].phase == Phase.Value('TRAIN'):
self.dp = LMDBDataProvider(params.data_param, params.transform_param, num_gpu)
else:
self.dp = LMDBDataProvider(params.data_param, params.transform_param, 1)
self.params = params
self.crop_size = params.transform_param.crop_size
self.generator = None
class LMDBDataUnit(DataUnit):
''' DataUnit load from LMDB.
:ivar caffe.LayerParameter params: lmdb data layer param defined by Caffe, params.data_param contains information about data source, parmas.transform_param mainly defines data augmentation operations
'''
def __init__(self, params, num_gpu):
super(LMDBDataUnit, self).__init__(params, num_gpu)
if params.include[0].phase == Phase.Value('TRAIN'):
self.dp = LMDBDataProvider(params.data_param,
params.transform_param, num_gpu)
else:
self.dp = LMDBDataProvider(params.data_param,
params.transform_param, 1)
self.params = params
self.crop_size = params.transform_param.crop_size
self.generator = None
def compute_size(self, from_btm, to_top):
self.out_shape = [
self.params.transform_param.crop_size,
self.params.transform_param.crop_size, 3, 1
]
to_top[self.top_names[0]] = self.out_shape[:]
def forward(self, from_btm, to_top, phase):
''' Feed-forward operation may vary according to phase.
.. note::
LMDB data provider now support multi-view testing, if phase is "MULTI_VIEW", it will produce concequtive 10 batches of different views of the same original image
'''
if self.generator == None:
if phase == 'TRAIN' or phase == 'TEST':
self.generator = self.dp.get_mb(phase)
#multiview test
else:
self.generator = self.dp.get_multiview_mb()
while True:
try:
(samples, labels) = next(self.generator)
if len(labels) == 0:
(samples, labels) = next(self.generator)
except StopIteration:
print 'Have scanned the whole dataset; start from the begginning agin'
self.generator = self.dp.get_mb(phase)
continue
break
to_top[self.top_names[0]] = owl.from_numpy(samples).reshape(
[self.crop_size, self.crop_size, 3, samples.shape[0]])
for i in range(1, len(self.top_names)):
to_top[self.top_names[i]] = labels[:, i - 1]
def __str__(self):
return 'lmdb_data'
class LMDBDataUnit(DataUnit):
''' DataUnit load from LMDB.
:ivar caffe.LayerParameter params: lmdb data layer param defined by Caffe, params.data_param contains information about data source, parmas.transform_param mainly defines data augmentation operations
'''
def __init__(self, params, num_gpu):
super(LMDBDataUnit, self).__init__(params, num_gpu)
if params.include[0].phase == Phase.Value('TRAIN'):
self.dp = LMDBDataProvider(params.data_param, params.transform_param, num_gpu)
else:
self.dp = LMDBDataProvider(params.data_param, params.transform_param, 1)
self.params = params
self.crop_size = params.transform_param.crop_size
self.generator = None
def compute_size(self, from_btm, to_top):
self.out_shape = [self.params.transform_param.crop_size,
self.params.transform_param.crop_size,
3, 1]
to_top[self.top_names[0]] = self.out_shape[:]
def forward(self, from_btm, to_top, phase):
''' Feed-forward operation may vary according to phase.
.. note::
LMDB data provider now support multi-view testing, if phase is "MULTI_VIEW", it will produce concequtive 10 batches of different views of the same original image
'''
if self.generator == None:
if phase == 'TRAIN' or phase == 'TEST':
self.generator = self.dp.get_mb(phase)
#multiview test
else:
self.generator = self.dp.get_multiview_mb()
while True:
try:
(samples, labels) = next(self.generator)
if len(labels) == 0:
(samples, labels) = next(self.generator)
except StopIteration:
print 'Have scanned the whole dataset; start from the begginning agin'
self.generator = self.dp.get_mb(phase)
continue
break
to_top[self.top_names[0]] = owl.from_numpy(samples).reshape(
[self.crop_size, self.crop_size, 3, samples.shape[0]])
for i in range (1, len(self.top_names)):
to_top[self.top_names[i]] = labels[:,i - 1]
def __str__(self):
return 'lmdb_data'
def __init__(self, params, num_gpu):
super(LMDBDataUnit, self).__init__(params, num_gpu)
if params.include[0].phase == Phase.Value('TRAIN'):
self.dp = LMDBDataProvider(params.data_param,
params.transform_param, num_gpu)
else:
self.dp = LMDBDataProvider(params.data_param,
params.transform_param, 1)
self.params = params
self.crop_size = params.transform_param.crop_size
self.generator = None
class LMDBDataUnit(DataUnit):
def __init__(self, params, num_gpu):
super(LMDBDataUnit, self).__init__(params, num_gpu)
if params.include[0].phase == Phase.Value('TRAIN'):
self.dp = LMDBDataProvider(params.data_param, params.transform_param, num_gpu)
else:
self.dp = LMDBDataProvider(params.data_param, params.transform_param, 1)
self.params = params
self.crop_size = params.transform_param.crop_size
self.generator = None
def init_layer_size(self, from_btm, to_top):
self.out_shape = [self.params.transform_param.crop_size,
self.params.transform_param.crop_size,
3, 1]
to_top[self.top_names[0]] = self.out_shape[:]
def forward(self, from_btm, to_top, phase):
if self.generator == None:
if phase == 'TRAIN' or phase == 'TEST':
self.generator = self.dp.get_mb(phase)
#multiview test
else:
self.generator = self.dp.get_multiview_mb()
while True:
try:
(samples, labels) = next(self.generator)
if len(labels) == 0:
(samples, labels) = next(self.generator)
except StopIteration:
print 'Have scanned the whole dataset; start from the begginning agin'
self.generator = self.dp.get_mb(phase)
continue
break
to_top[self.top_names[0]] = owl.from_numpy(samples).reshape(
[self.crop_size, self.crop_size, 3, samples.shape[0]])
for i in range (1, len(self.top_names)):
to_top[self.top_names[i]] = labels[:,i - 1]
def __str__(self):
return 'lmdb_data'
class LMDBDataUnit(DataUnit):
''' DataUnit load from LMDB.
:ivar caffe.LayerParameter params: lmdb data layer param defined by Caffe, params.data_param contains information about data source, parmas.transform_param mainly defines data augmentation operations
'''
def __init__(self, params, num_gpu):
super(LMDBDataUnit, self).__init__(params, num_gpu)
if params.include[0].phase == Phase.Value('TRAIN'):
self.dp = LMDBDataProvider(params.data_param, params.transform_param, num_gpu)
else:
self.dp = LMDBDataProvider(params.data_param, params.transform_param, 1)
self.params = params
self.crop_size = params.transform_param.crop_size
self.generator = None
self.out = None
self.multiview = False
def compute_size(self, from_btm, to_top):
self.out_shape = [self.params.transform_param.crop_size,
self.params.transform_param.crop_size,
3, 1]
to_top[self.top_names[0]] = dict()
to_top[self.top_names[0]]['out_shape'] = self.out_shape[:]
to_top[self.top_names[0]]['rec_on_ori'] = 1
to_top[self.top_names[0]]['stride_on_ori'] = 1
to_top[self.top_names[0]]['start_on_ori'] = 0
self.rec_on_ori = 1
self.stride_on_ori = 1
self.start_on_ori = 0
def forward(self, from_btm, to_top, phase):
''' Feed-forward operation may vary according to phase.
.. note::
LMDB data provider now support multi-view testing, if multiview == True, it will produce concequtive 10 batches of different views of the same original image
'''
if self.generator == None:
if self.multiview == False:
self.generator = self.dp.get_mb(phase)
#multiview test
else:
self.generator = self.dp.get_multiview_mb()
while True:
try:
(samples, labels) = next(self.generator)
if len(labels) == 0:
(samples, labels) = next(self.generator)
except StopIteration:
print 'Have scanned the whole dataset; start from the begginning agin'
if self.multiview == False:
self.generator = self.dp.get_mb(phase)
#multiview test
else:
self.generator = self.dp.get_multiview_mb()
continue
break
#TODO(Jesse Lovitt): Change this 256 to a division by 256/max-fixed-point-value
to_top[self.top_names[0]] = owl.from_numpy(np.divide(samples,256)).reshape(
[self.crop_size, self.crop_size, 3, samples.shape[0]])
for i in range (1, len(self.top_names)):
to_top[self.top_names[i]] = labels[:,i - 1]
#to_top[self.top_names[0]] = owl.zeros([self.crop_size, self.crop_size, 3, 256])
#for i in range (1, len(self.top_names)):
#to_top[self.top_names[i]] = np.ones(256)
self.out = to_top[self.top_names[0]]
def __str__(self):
return 'lmdb_data'
